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  Method and system for generating object classification modelsUSPTO Application #: 20060074829Title: Method and system for generating object classification models Abstract: A method for generating object classification models is disclosed. Initially, a set of training data is fed into a training algorithm to generate a first object classification model. A set of field data is then applied to the first object classification model to produce a set of field object classifications. The number of data in the set of field data is significantly less than the number of data in the set of training data. Finally, the set of field object classifications and the set of field data are fed into the training algorithm to generate a second object classification model. The second object classification model can be utilized for predicting object classifications. (end of abstract)
Agent: Dillon & Yudell LLP - Austin, TX, US Inventors: Ameha Aklilu, Raed Hijer, Wilson Velez USPTO Applicaton #: 20060074829 - Class: 706020000 (USPTO) Related Patent Categories: Data Processing: Artificial Intelligence, Neural Network, Learning Task, Classification Or Recognition The Patent Description & Claims data below is from USPTO Patent Application 20060074829. Brief Patent Description - Full Patent Description - Patent Application Claims
BACKGROUND OF THE INVENTION [0001] 1. Technical Field [0002] The present invention relates to data mining in general, and, in particular, to a method and system for providing machine learning. Still more particularly, the present invention relates to a method and system for generating object classification models. [0003] 2. Description of Related Art [0004] The objective of object classification (often referred to as supervised learning) is to find an approximation (or hypothesis) to a target concept that assigns objects (such as processes or events) into different classifications (or classes). Generally speaking, object classification can be divided into two phases, namely, a learning phase and a testing phase. [0005] The goal of a learning phase is to learn how to classify objects by finding correlations among object descriptions. During the learning phase, a learning algorithm is applied to a set of training data that typically includes object descriptions (feature variables) together with the correct classification for each object (class variable) for constructing an object classification model capable of predicting a class variable of a record in which the feature variables are known but the class variable is unknown. Thus, the end result of the learning phase is an object classification model that can be used to predict classes of new objects. [0006] During the testing phase, the object classification model derived in the training phase is utilized to predict the classes of a set of testing objects. The object classes predicted by the object classification model are subsequently compared to the true object classes to determine the accuracy of the object classification model. [0007] While most learning algorithms of conventional object classification systems can produce sufficiently accurate object classification models for many applications, they suffer from a number of limitations. Specifically, the learning algorithms of conventional object classification systems are unable to adapt over time. In other words, once an object classification model had been generated by a learning algorithm, the object classification model cannot be reconfigured based on new experiences. Thus, conventional object classification systems that employ such object classification models are prone to repeating the same errors. [0008] Consequently, it would be desirable to provide an improved object classification system. SUMMARY OF THE INVENTION [0009] In accordance with a preferred embodiment of the present invention, a set of training data is fed into a training algorithm to generate a first object classification model. A set of field data is then applied to the first object classification model to produce a set of field object classifications. The number of data in the set of field data is significantly less than the number of data in the set of training data. Finally, the set of field object classifications and the set of field data are fed into the training algorithm to generate a second object classification model. The second object classification model can be utilized for predicting object classifications. [0010] All features and advantages of the present invention will become apparent in the following detailed written description. BRIEF DESCRIPTION OF THE DRAWINGS [0011] The invention itself, as well as a preferred mode of use, further objects, and advantages thereof, will best be understood by reference to the following detailed description of an illustrative embodiment when read in conjunction with the accompanying drawings, wherein: [0012] FIG. 1 is a block diagram of an object classification system, in accordance with a preferred embodiment of the present invention; [0013] FIG. 2 is an object table within the object classification system from FIG. 1, in accordance with a preferred embodiment of the present invention; [0014] FIG. 3 is a rule table within the object classification system from FIG. 1, in accordance with a preferred embodiment of the present invention; and [0015] FIG. 4 is a high-level logic flow diagram of a method for generating an object classification model, in accordance with a preferred embodiment of the present invention. DETAILED DESCRIPTION OF A PREFERRED EMBODIMENT [0016] Referring now to the drawings and in particular to FIG. 1, there is depicted a block diagram of an object classification system, in accordance with a preferred embodiment of the present invention. As shown, an object classification system 10 includes a processor 11 and a data storage device 12. Data storage device 12 stores instructions for processor 11 to retrieve, interpret and execute. [0017] Data storage device 12 preferably includes a domain dataset 13, performance datasets 14 and rules of experience tables 15. Domain dataset 13 contains a record for each object and its associated class. Performance datasets 14 contain a learning algorithm that can generate an object classification model for a domain. Rules of experience tables 15 identify a number of prioritized rules and their corresponding conditions, which if satisfied, provide a bias (or outcome) that should be employed during the generation of an object classification model. [0018] In addition, data storage device 12 includes a meta-feature generation process 16, a performance assessment process 17, a rules of experience generation process 18 and a self-adaptive learning process 19. Meta-feature generation process 16 processes each domain dataset to represent the domain as a set of meta-features. Performance assessment process 17 evaluates the performance of a given object classification model for a given domain dataset described by a set of meta-features, and then stores the results in performance datasets 14. Rules of experience generation process 18 evaluates performance datasets 14 in order to modify or extend the rules currently stored within rules of experience tables 15. Self-adaptive learning process 19 identifies the best object classification model for a given domain dataset 13 according to the rules currently stored within rules of experience tables 15. [0019] With reference now to FIG. 2, there is depicted an object table within domain dataset 13 (from FIG. 1), in accordance with a preferred embodiment of the present invention. As shown, an object table 20 maintains multiple records, such as records 21a-21n, each associated with a different object. Each of records 21a-21n contains multiple feature fields, such as feature fields 22a-22n, for describing various features of the associated object. Each of records 21a-21n also contains a class field, such as a class field 23, for describing the class of the associated object. [0020] Referring now to FIG. 3, there is illustrated a rule table within rules of experience tables 15 (from FIG. 1), in accordance with a preferred embodiment of the present invention. As shown, a rule table 30 includes multiple rules, such as rules 31a-31n. Each of rules 31a-31n is identified by a rule number stored in a rule identifier field 32. Each of rules 31a-31n is associated with a condition stored in a rule condition field 33. In addition, each of rules 31a-31n is associated with a bias (or outcome) stored in a bias field 34. When one of rules 31a-31n within rule table 30 is satisfied, the corresponding bias stored within bias field 34 will be employed during the generation of an object classification model. Continue reading... Full patent description for Method and system for generating object classification models Brief Patent Description - Full Patent Description - Patent Application Claims Click on the above for other options relating to this Method and system for generating object classification models patent application. ###  How KEYWORD MONITOR works... a FREE service from FreshPatents1. Sign up (takes 30 seconds). 2. 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